Various methods and systems are provided for thermal monitoring of tissue with an ultrasound imaging system. In one embodiment, a method comprises acquiring, via an ultrasound probe, an ultrasound image, selecting at least one region of interest in the ultrasound image, extracting features from the at least one region of interest, classifying a thermal state of tissue in the at least one region of interest based on the features, and outputting, via a display device, the thermal state of the tissue. In this way, ultrasound imaging may be used for real-time thermal monitoring of tissue during an ablation procedure, thereby improving the accuracy and efficacy of the ablation procedure.

Machine learning architectures configured to perform pattern recognition using a structurally regularized convolutional neural network architecture, along with corresponding methods of operation, are provided. One such architecture includes a memory, and a processor coupled to the memory and configured to: receive data comprising a pattern to be recognized, decompose the data into a plurality of sub-bands, process each of the plurality of sub-bands with a respective convolutional neural network (CNN) to generate a plurality of outputs, where each of the CNNs operates independently of the other CNNs, aggregate the outputs of the CNNs, and train, using the aggregated output, the CNNs to recognize the pattern.

The present disclosure provides a method for recognizing and diagnosing transformer equipment based on image fusion and target recognition, and relates to the technical field of recognizing power equipment. The method includes: performing, through wavelet transform, fusion on an infrared image and a visible light image that corresponds to equipment, and constructing a high-quality library of a fused infrared image and a fused visible light image; and training, based on an SSD target recognition algorithm, with the dataset that uses the library of the fused images as an algorithm, to intelligently recognize the fused image. The target recognition method for the fused images has advantages that infrared light is not affected by light, temperature information is provided, and a resolution of visible light is clear, so that accurate recognition and diagnosis on transformer equipment are implemented.

The present disclosure provides a method for recognizing and diagnosing transformer equipment based on image fusion and target recognition, and relates to the technical field of recognizing power equipment. The method includes: performing, through wavelet transform, fusion on an infrared image and a visible light image that corresponds to equipment, and constructing a high-quality library of a fused infrared image and a fused visible light image; and training, based on an SSD target recognition algorithm, with the dataset that uses the library of the fused images as an algorithm, to intelligently recognize the fused image. The target recognition method for the fused images has advantages that infrared light is not affected by light, temperature information is provided, and a resolution of visible light is clear, so that accurate recognition and diagnosis on transformer equipment are implemented.

An image processing method includes acquiring an image in which a cell structure having a stained vascular network structure is imaged, applying a wavelet transform to the image such that a contour image in which contours of the vascular network structure are extracted is generated, and repeatedly excluding object pixels from object boundaries recognized in the contour image such that a skeleton image in which skeletons having a line width of a predetermined number of pixels are extracted is generated.

An image processing method includes acquiring an image in which a cell structure having a stained vascular network structure is imaged, applying a wavelet transform to the image such that a contour image in which contours of the vascular network structure are extracted is generated, and repeatedly excluding object pixels from object boundaries recognized in the contour image such that a skeleton image in which skeletons having a line width of a predetermined number of pixels are extracted is generated.

The present disclosure relates to systems and methods for object recognition. The system may obtain an image and a model. The image may include a search region in which the object recognition process is performed. In the objection recognition process, for each of one or more sub-regions of the search region, the system may determine a match metric indicating a similarity between the model and the sub-region of the search region. Further, the system may determine an instance of the model among the one or more sub-regions of the search region based on the match metrics.

The present disclosure relates to systems and methods for object recognition. The system may obtain an image and a model. The image may include a search region in which the object recognition process is performed. In the objection recognition process, for each of one or more sub-regions of the search region, the system may determine a match metric indicating a similarity between the model and the sub-region of the search region. Further, the system may determine an instance of the model among the one or more sub-regions of the search region based on the match metrics.

Disclosed are multiple object detection method and apparatus. The multiple object detection apparatus includes a feature map extraction unit for extracting a plurality of multi-scale feature maps based on an input image, and a feature map fusion unit for generating a multi-scale fusion feature map including context information by fusing adjacent multi-scale feature maps among the plurality of multi-scale feature maps generated by the feature map extraction unit.

Disclosed are multiple object detection method and apparatus. The multiple object detection apparatus includes a feature map extraction unit for extracting a plurality of multi-scale feature maps based on an input image, and a feature map fusion unit for generating a multi-scale fusion feature map including context information by fusing adjacent multi-scale feature maps among the plurality of multi-scale feature maps generated by the feature map extraction unit.